Learning Analogies and Semantic Relations

We present an algorithm for learning from unlabeled text, based on the Vector Space Model (VSM) of information retrieval, that can solve verbal analogy questions of the kind found in the Scholastic Aptitude Test (SAT). A verbal analogy has the form A:B::C:D, meaning "A is to B as C is to D"; for example, mason:stone::carpenter:wood. SAT analogy questions provide a word pair, A:B, and the problem is to select the most analogous word pair, C:D, from a set of five choices. The VSM algorithm correctly answers 47% of a collection of 374 college-level analogy questions (random guessing would yield 20% correct). We motivate this research by relating it to work in cognitive science and linguistics, and by applying it to a difficult problem in natural language processing, determining semantic relations in noun-modifier pairs. The problem is to classify a noun-modifier pair, such as "laser printer", according to the semantic relation between the noun (printer) and the modifier (laser). We use a supervised nearest-neighbour algorithm that assigns a class to a given noun-modifier pair by finding the most analogous noun-modifier pair in the training data. With 30 classes of semantic relations, on a collection of 600 labeled noun-modifier pairs, the learning algorithm attains an F value of 26.5% (random guessing: 3.3%). With 5 classes of semantic relations, the F value is 43.2% (random: 20%). The performance is state-of-the-art for these challenging problems

LT3: sentiment analysis of figurative tweets: piece of cake #NotReally

This paper describes our contribution to the SemEval-2015 Task 11 on sentiment analysis of figurative language in Twitter. We considered two approaches, classification and regression, to provide fine-grained sentiment scores for a set of tweets that are rich in sarcasm, irony and metaphor. To this end, we combined a variety of standard lexical and syntactic features with specific features for capturing figurative content. All experiments were done using supervised learning with LIBSVM. For both runs, our system ranked fourth among fifteen submissions

Metaphor Aptness And Conventionality: A Processing Fluency Account

Conventionality and aptness are two dimensions of metaphorical sentences thought to play an important role in determining how quick and easy it is to process a metaphor. Conventionality reflects the familiarity of a metaphor whereas aptness reflects the degree to which a metaphor vehicle captures important features of a metaphor topic. In recent years it has become clear that operationalizing these two constructs is not as simple as asking naïve raters for subjective judgments. It has been found that ratings of aptness and conventionality are highly correlated, which has led some researchers to pursue alternative methods for measuring the constructs. Here, in four experiments, we explore the underlying reasons for the high correlation in ratings of aptness and conventionality, and question the construct validity of various methods for measuring the two dimensions. We find that manipulating the processing fluency of a metaphorical sentence by means of familiarization to similar senses of the metaphor (“in vivo conventionalization”) influences ratings of the sentence\u27s aptness. This misattribution may help explain why subjective ratings of aptness and conventionality are highly correlated. In addition, we find other reasons to question the construct validity of conventionality and aptness measures: for instance, we find that conventionality is context dependent and thus not attributable to a metaphor vehicle alone, and we find that ratings of aptness take more into account than they should

Metaphor as categorisation: a connectionist implementation

A key issue for models of metaphor comprehension is to explain how in some metaphorical comparison , only some features of B are transferred to A. The features of B that are transferred to A depend both on A and on B. This is the central thrust of Black's well known interaction theory of metaphor comprehension (1979). However, this theory is somewhat abstract, and it is not obvious how it may be implemented in terms of mental representations and processes. In this paper we describe a simple computational model of on-line metaphor comprehension which combines Black's interaction theory with the idea that metaphor comprehension is a type of categorisation process (Glucksberg & Keysar, 1990, 1993). The model is based on a distributed connectionist network depicting semantic memory (McClelland & Rumelhart, 1986). The network learns feature-based information about various concepts. A metaphor is comprehended by applying a representation of the first term A to the network storing knowledge of the second term B, in an attempt to categorise it as an exemplar of B. The output of this network is a representation of A transformed by the knowledge of B. We explain how this process embodies an interaction of knowledge between the two terms of the metaphor, how it accords with the contemporary theory of metaphor stating that comprehension for literal and metaphorical comparisons is carried out by identical mechanisms (Gibbs, 1994), and how it accounts for both existing empirical evidence (Glucksberg, McGlone, & Manfredi, 1997) and generates new predictions. In this model, the distinction between literal and metaphorical language is one of degree, not of kind

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

A connectionist account of the emergence of the literal-metaphorical-anomalous distinction in young children

We present the first developmental computational model of metaphor comprehension, which seeks to relate the emergence of a distinction between literal and non-literal similarity in young children to the development of semantic representations. The model gradually learns to distinguish literal from metaphorical semantic juxtapositions as it acquires more knowledge about the vehicle domain. In accordance with Keil (1986), the separation of literal from metaphorical comparisons is found to depend on the maturity of the vehicle concept stored within the network. The model generates a number of explicit novel predictions

Developing cross‐cultural communicative competence via computer‐assisted language learning: The case of pre‐service ESL/EFL teachers

Based on a qualitative research project, this article presents a view on the use of computer technology to develop a critical cross‐cultural communicative competence in English as a Second Language (ESL) / English as a Foreign Language (EFL) for pre‐service teachers. The article includes a brief critical theoretical framework, some classroom pedagogical implications, and a data‐based discussion of pre‐service teachers’ views. These views included: (1) critical views and an awareness of cultural power relations in computer‐assisted language learning (CALL), (2) uncritical views and a lack of awareness of cultural power relations in CALL, and (3) uses of metaphors in CALL. The powerful contribution of CALL can be found in its potential for providing ways to connect people and build communities, for offering opportunities for cross‐cultural communicative competence to be developed and used, and for improving processes of democratization via computer‐mediated communication. However, a socio‐cultural criticism revealed that this powerful tool, like any other media, is non‐neutral because it can serve to reinforce further the hegemonic aspects of education, that is, the dominant culture will be strengthened and protected via computer technology. Computer‐based technologies and software are increasingly incorporated into the curricula of ESLIEFL teacher education programmes. However, this integration is often done in ways that seem to leave unquestioned the potential cultural and hegemonic ramifications of such technology. Hence there is a need for a more critical technological competence

The pre-scientific concept of a "soul": A neurophenomenological hypothesis about its origin.

In this contribution I will argue that our traditional, folk-phenomenological concept of a "soul� may have its origins in accurate and truthful first-person reports about the experiential content of a specific neurophenomenological state-class. This class of phenomenal states is called the "Out-of-body experience� (OBE hereafter), and I will offer a detailed description in section 3 of this paper. The relevant type of conscious experience seems to possess a culturally invariant cluster of functional and phenomenal core properties: it is a specific kind of conscious experience, which can in principle be undergone by every human being. I propose that it probably is one of the most central semantic roots of our everyday, folk-phenomenological idea of what a soul actually is

The Case for Dynamic Models of Learners' Ontologies in Physics

In a series of well-known papers, Chi and Slotta (Chi, 1992; Chi & Slotta, 1993; Chi, Slotta & de Leeuw, 1994; Slotta, Chi & Joram, 1995; Chi, 2005; Slotta & Chi, 2006) have contended that a reason for students' difficulties in learning physics is that they think about concepts as things rather than as processes, and that there is a significant barrier between these two ontological categories. We contest this view, arguing that expert and novice reasoning often and productively traverses ontological categories. We cite examples from everyday, classroom, and professional contexts to illustrate this. We agree with Chi and Slotta that instruction should attend to learners' ontologies; but we find these ontologies are better understood as dynamic and context-dependent, rather than as static constraints. To promote one ontological description in physics instruction, as suggested by Slotta and Chi, could undermine novices' access to productive cognitive resources they bring to their studies and inhibit their transition to the dynamic ontological flexibility required of experts.Comment: The Journal of the Learning Sciences (In Press